Daily AI Briefing — June 3, 2026

Building on yesterday's Social unveiling of RTX Spark, now part of the broader Nvidia roundup, NVIDIA launched Cosmos 3, a Mixture-of-Transformers world model unifying language, image, video, audio, and action with autoregressive reasoner and diffusion generator towers, claiming new SOTA open-weights image and video generation. It also unveiled Nemotron 3 Ultra, a 550B-A55B open-weights LLM billed as the new US SOTA, plus the RTX Spark personal supercomputer.

Microsoft announced MAI-Thinking-1, its first advanced in-house reasoning model, described as medium-sized and matching leading models on key software-engineering benchmarks. Microsoft says it was trained from scratch on clean data without distillation from third-party models, as it loosens ties with OpenAI.

Trump signed an executive order creating a voluntary framework for the federal government to vet powerful new AI models before public release, focused on cybersecurity and national security risks. The voluntary nature signals continued reluctance to impose binding rules.

Alphabet's shares dropped after announcing an unprecedented $80bn equity raise to fund AI infrastructure, while Anthropic confidentially filed for a US IPO. The roundup also notes warnings that AI could drive up youth unemployment.

Berkshire Hathaway is investing $10 billion in Alphabet's $80 billion AI infrastructure raise, with Alphabet expecting 2026 capital spending to reach $190 billion. The investment marks a notable bet on the AI buildout.

Building on yesterday's News announcement, here's the full Cosmos 3 technical paper, Cosmos 3 from NVIDIA is a family of omnimodal world models that jointly process and generate language, image, video, audio, and action within a unified mixture-of-transformers architecture, subsuming vision-language models, video generators, world simulators, and world-action models. It claims state-of-the-art across understanding and generation tasks for Physical AI.

NVIDIA's OmniDreams is a foundation generative world model, post-trained from the Cosmos diffusion model, for real-time closed-loop autonomous vehicle simulation that autoregressively generates action-conditioned sensor observations. Addresses long-tail scenario evaluation beyond reconstruction-based simulators.

LEAP is an agentic framework enabling general-purpose foundation models to achieve state-of-the-art automated formal theorem proving in Lean by decomposing problems and iterating with the Lean compiler. It introduces Lean-IMO-Bench for rigorous evaluation.

Derives closed-form near-optimal linear blockwise transforms for joint weight-activation LLM quantization, called WUSH, combining a Hadamard backbone with a data-dependent second-moment component. It provides provable near-optimality for both integer and floating-point quantizers.

This paper presents the first framework for establishing refusal boundaries for AI agents in offensive cybersecurity contexts, defining principled refusal criteria, task categories warranting refusal, and an evaluation methodology under benign and adversarial conditions. It complements proficiency-focused cyber benchmarks with a safety-refusal dimension.

Ethan Mollick describes a study where Gemini 2.5 answered law professors' office-hours questions and beat human professors with a 75 percent win rate while being rated less harmful, with newer models doing even better.

Anthropic publicly welcomes a US Executive Order on AI and signals intent to collaborate with the White House on implementation.

Ethan Mollick summarizes a big paper using GitHub data showing autocomplete tools led to 2.2x more code, local agents 7.4x, and remote coding agents 17.3x, but human bottlenecks meant releases only rose 30 percent.